Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations2701455
Missing cells632235
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory758.3 MiB
Average record size in memory294.4 B

Variable types

Numeric16
Categorical3

Alerts

canal is highly overall correlated with segmentoHigh correlation
densidad_hab is highly overall correlated with superficie_km2High correlation
dolar is highly overall correlated with id_periodo and 3 other fieldsHigh correlation
id_barrio is highly overall correlated with id_comunaHigh correlation
id_comuna is highly overall correlated with id_barrioHigh correlation
id_periodo is highly overall correlated with dolar and 3 other fieldsHigh correlation
imacec is highly overall correlated with dolar and 3 other fieldsHigh correlation
segmento is highly overall correlated with canalHigh correlation
superficie_km2 is highly overall correlated with densidad_habHigh correlation
tpm is highly overall correlated with dolar and 3 other fieldsHigh correlation
uf is highly overall correlated with dolar and 3 other fieldsHigh correlation
descr_flag_patente is highly imbalanced (53.6%)Imbalance
indice_gse has 147275 (5.5%) missing valuesMissing
n_habitantes has 147275 (5.5%) missing valuesMissing
n_ptos_interes has 48031 (1.8%) missing valuesMissing
superficie_km2 has 48031 (1.8%) missing valuesMissing
densidad_hab has 147275 (5.5%) missing valuesMissing
tasa_desempleo has 82402 (3.1%) missing valuesMissing
liq_um is highly skewed (γ1 = 91.28986202)Skewed
liq_um has 27516 (1.0%) zerosZeros
ipc has 82125 (3.0%) zerosZeros
imacec has 168331 (6.2%) zerosZeros

Reproduction

Analysis started2025-10-18 02:22:52.936293
Analysis finished2025-10-18 02:25:21.909728
Duration2 minutes and 28.97 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id_cliente
Real number (ℝ)

Distinct131135
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean374215.96
Minimum33
Maximum727518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:21.940211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile57323
Q1193464
median373932
Q3554316
95-th percentile688892
Maximum727518
Range727485
Interquartile range (IQR)360852

Descriptive statistics

Standard deviation203347.58
Coefficient of variation (CV)0.54339632
Kurtosis-1.1908105
Mean374215.96
Median Absolute Deviation (MAD)180395
Skewness-0.057548703
Sum1.0109276 × 1012
Variance4.1350237 × 1010
MonotonicityIncreasing
2025-10-17T23:25:21.999274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72751832
 
< 0.1%
72751232
 
< 0.1%
4332
 
< 0.1%
4432
 
< 0.1%
72750732
 
< 0.1%
4832
 
< 0.1%
5132
 
< 0.1%
72711832
 
< 0.1%
72711032
 
< 0.1%
72710632
 
< 0.1%
Other values (131125)2701135
> 99.9%
ValueCountFrequency (%)
3322
< 0.1%
4332
< 0.1%
4432
< 0.1%
4725
< 0.1%
4832
< 0.1%
5132
< 0.1%
5624
< 0.1%
5718
< 0.1%
605
 
< 0.1%
6219
< 0.1%
ValueCountFrequency (%)
72751832
< 0.1%
7275176
 
< 0.1%
72751525
< 0.1%
7275141
 
< 0.1%
72751232
< 0.1%
72751127
< 0.1%
72751029
< 0.1%
7275098
 
< 0.1%
72750732
< 0.1%
72750529
< 0.1%

id_periodo
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202392.25
Minimum202301
Maximum202508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:22.055094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum202301
5-th percentile202302
Q1202308
median202404
Q3202412
95-th percentile202507
Maximum202508
Range207
Interquartile range (IQR)104

Descriptive statistics

Standard deviation77.300609
Coefficient of variation (CV)0.00038193463
Kurtosis-1.3257742
Mean202392.25
Median Absolute Deviation (MAD)95
Skewness0.22741951
Sum5.4675356 × 1011
Variance5975.3841
MonotonicityNot monotonic
2025-10-17T23:25:22.104045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
20230388568
 
3.3%
20230187589
 
3.2%
20230486882
 
3.2%
20231286329
 
3.2%
20230286291
 
3.2%
20230586184
 
3.2%
20241285704
 
3.2%
20240385662
 
3.2%
20231085406
 
3.2%
20231185351
 
3.2%
Other values (22)1837489
68.0%
ValueCountFrequency (%)
20230187589
3.2%
20230286291
3.2%
20230388568
3.3%
20230486882
3.2%
20230586184
3.2%
20230684554
3.1%
20230784431
3.1%
20230885270
3.2%
20230984846
3.1%
20231085406
3.2%
ValueCountFrequency (%)
20250882125
3.0%
20250781864
3.0%
20250680950
3.0%
20250582108
3.0%
20250482402
3.1%
20250384329
3.1%
20250282334
3.0%
20250184312
3.1%
20241285704
3.2%
20241184756
3.1%

liq_um
Real number (ℝ)

Skewed  Zeros 

Distinct516419
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7252702
Minimum-286.8768
Maximum9744.3456
Zeros27516
Zeros (%)1.0%
Negative352
Negative (%)< 0.1%
Memory size20.6 MiB
2025-10-17T23:25:22.160111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-286.8768
5-th percentile0.216
Q10.996
median2.54384
Q35.987984
95-th percentile19.369454
Maximum9744.3456
Range10031.222
Interquartile range (IQR)4.991984

Descriptive statistics

Standard deviation38.825173
Coefficient of variation (CV)5.773028
Kurtosis15670.431
Mean6.7252702
Median Absolute Deviation (MAD)1.89584
Skewness91.289862
Sum18168015
Variance1507.3941
MonotonicityNot monotonic
2025-10-17T23:25:22.215894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
027516
 
1.0%
0.1927009
 
0.3%
0.2886897
 
0.3%
0.3846768
 
0.3%
0.0966433
 
0.2%
0.486411
 
0.2%
0.726144
 
0.2%
0.13446133
 
0.2%
0.246032
 
0.2%
0.365799
 
0.2%
Other values (516409)2616313
96.8%
ValueCountFrequency (%)
-286.87681
< 0.1%
-87.079681
< 0.1%
-75.81121
< 0.1%
-44.9281
< 0.1%
-44.14081
< 0.1%
-38.3041
< 0.1%
-34.8481
< 0.1%
-33.61
< 0.1%
-32.72641
< 0.1%
-32.60161
< 0.1%
ValueCountFrequency (%)
9744.34561
< 0.1%
9635.23681
< 0.1%
9074.41
< 0.1%
8754.95041
< 0.1%
8635.97761
< 0.1%
8626.7041
< 0.1%
8339.40481
< 0.1%
8238.078721
< 0.1%
7922.161
< 0.1%
7911.361
< 0.1%

id_barrio
Real number (ℝ)

High correlation 

Distinct2037
Distinct (%)0.1%
Missing11946
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean889380.19
Minimum110110
Maximum1510511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:22.273405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum110110
5-th percentile210211
Q1560114
median840116
Q31311415
95-th percentile1360112
Maximum1510511
Range1400401
Interquartile range (IQR)751301

Descriptive statistics

Standard deviation390355.34
Coefficient of variation (CV)0.43890717
Kurtosis-1.2106088
Mean889380.19
Median Absolute Deviation (MAD)419697
Skewness-0.1720716
Sum2.391996 × 1012
Variance1.5237729 × 1011
MonotonicityNot monotonic
2025-10-17T23:25:22.329876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131012413475
 
0.5%
21011912620
 
0.5%
81021310930
 
0.4%
13125109575
 
0.4%
10101219469
 
0.4%
2101149420
 
0.3%
1107109301
 
0.3%
13124199002
 
0.3%
3202108753
 
0.3%
10301238153
 
0.3%
Other values (2027)2588811
95.8%
(Missing)11946
 
0.4%
ValueCountFrequency (%)
1101106749
0.2%
1101113114
0.1%
1101124491
0.2%
1101133293
0.1%
1101143706
0.1%
1101152525
 
0.1%
1101165981
0.2%
1101173686
0.1%
1101183944
0.1%
110119572
 
< 0.1%
ValueCountFrequency (%)
15105111363
 
0.1%
15105105
 
< 0.1%
1510128230
 
< 0.1%
15101261778
 
0.1%
1510125696
 
< 0.1%
15101243883
0.1%
15101232424
0.1%
15101225846
0.2%
15101211388
 
0.1%
15101202577
0.1%

id_comuna
Real number (ℝ)

High correlation 

Distinct325
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8897.1978
Minimum1101
Maximum15105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:22.409331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1101
5-th percentile2102
Q15601
median8401
Q313114
95-th percentile13601
Maximum15105
Range14004
Interquartile range (IQR)7513

Descriptive statistics

Standard deviation3901.4076
Coefficient of variation (CV)0.43849847
Kurtosis-1.2075814
Mean8897.1978
Median Absolute Deviation (MAD)4197
Skewness-0.17462584
Sum2.4035379 × 1010
Variance15220982
MonotonicityNot monotonic
2025-10-17T23:25:22.470735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1310186070
 
3.2%
210172197
 
2.7%
510958552
 
2.2%
1320155718
 
2.1%
510150436
 
1.9%
1510147535
 
1.8%
610146367
 
1.7%
1311046116
 
1.7%
1311945493
 
1.7%
410245386
 
1.7%
Other values (315)2147585
79.5%
ValueCountFrequency (%)
110142577
1.6%
110716052
 
0.6%
14011633
 
0.1%
210172197
2.7%
21024098
 
0.2%
21031381
 
0.1%
21043983
 
0.1%
220134964
1.3%
22034691
 
0.2%
23018041
 
0.3%
ValueCountFrequency (%)
151051394
 
0.1%
1510147535
1.8%
142044218
 
0.2%
142031763
 
0.1%
142022966
 
0.1%
142014589
 
0.2%
141085340
 
0.2%
141072986
 
0.1%
141063807
 
0.1%
141041736
 
0.1%

segmento
Categorical

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size131.4 MiB
AL
1505965 
AP
488044 
BO
232348 
RT
177467 
FS
 
118931
Other values (11)
178700 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5402910
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBA
2nd rowBA
3rd rowBA
4th rowBA
5th rowBA

Common Values

ValueCountFrequency (%)
AL1505965
55.7%
AP488044
 
18.1%
BO232348
 
8.6%
RT177467
 
6.6%
FS118931
 
4.4%
MA44579
 
1.7%
KI32807
 
1.2%
BA29670
 
1.1%
IE24701
 
0.9%
FF22927
 
0.8%
Other values (6)24016
 
0.9%

Length

2025-10-17T23:25:22.533231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
al1505965
55.7%
ap488044
 
18.1%
bo232348
 
8.6%
rt177467
 
6.6%
fs118931
 
4.4%
ma44579
 
1.7%
ki32807
 
1.2%
ba29670
 
1.1%
ie24701
 
0.9%
ff22927
 
0.8%
Other values (6)24016
 
0.9%

Most occurring characters

ValueCountFrequency (%)
A2068258
38.3%
L1505965
27.9%
P488044
 
9.0%
B262018
 
4.8%
O232348
 
4.3%
R189074
 
3.5%
T177467
 
3.3%
F164929
 
3.1%
S125060
 
2.3%
I60069
 
1.1%
Other values (5)129678
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)5402910
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A2068258
38.3%
L1505965
27.9%
P488044
 
9.0%
B262018
 
4.8%
O232348
 
4.3%
R189074
 
3.5%
T177467
 
3.3%
F164929
 
3.1%
S125060
 
2.3%
I60069
 
1.1%
Other values (5)129678
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5402910
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A2068258
38.3%
L1505965
27.9%
P488044
 
9.0%
B262018
 
4.8%
O232348
 
4.3%
R189074
 
3.5%
T177467
 
3.3%
F164929
 
3.1%
S125060
 
2.3%
I60069
 
1.1%
Other values (5)129678
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5402910
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A2068258
38.3%
L1505965
27.9%
P488044
 
9.0%
B262018
 
4.8%
O232348
 
4.3%
R189074
 
3.5%
T177467
 
3.3%
F164929
 
3.1%
S125060
 
2.3%
I60069
 
1.1%
Other values (5)129678
 
2.4%

canal
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 MiB
COMPRA
1738313 
CONSUMO
918563 
MAYORISTA
 
44579

Length

Max length9
Median length6
Mean length6.3895308
Min length6

Characters and Unicode

Total characters17261030
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCONSUMO
2nd rowCONSUMO
3rd rowCONSUMO
4th rowCONSUMO
5th rowCONSUMO

Common Values

ValueCountFrequency (%)
COMPRA1738313
64.3%
CONSUMO918563
34.0%
MAYORISTA44579
 
1.7%

Length

2025-10-17T23:25:22.577331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T23:25:22.609966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
compra1738313
64.3%
consumo918563
34.0%
mayorista44579
 
1.7%

Most occurring characters

ValueCountFrequency (%)
O3620018
21.0%
M2701455
15.7%
C2656876
15.4%
A1827471
10.6%
R1782892
10.3%
P1738313
10.1%
S963142
 
5.6%
N918563
 
5.3%
U918563
 
5.3%
Y44579
 
0.3%
Other values (2)89158
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)17261030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O3620018
21.0%
M2701455
15.7%
C2656876
15.4%
A1827471
10.6%
R1782892
10.3%
P1738313
10.1%
S963142
 
5.6%
N918563
 
5.3%
U918563
 
5.3%
Y44579
 
0.3%
Other values (2)89158
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17261030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O3620018
21.0%
M2701455
15.7%
C2656876
15.4%
A1827471
10.6%
R1782892
10.3%
P1738313
10.1%
S963142
 
5.6%
N918563
 
5.3%
U918563
 
5.3%
Y44579
 
0.3%
Other values (2)89158
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17261030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O3620018
21.0%
M2701455
15.7%
C2656876
15.4%
A1827471
10.6%
R1782892
10.3%
P1738313
10.1%
S963142
 
5.6%
N918563
 
5.3%
U918563
 
5.3%
Y44579
 
0.3%
Other values (2)89158
 
0.5%

descr_flag_patente
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size154.5 MiB
SIN PATENTE
1866826 
CON PATENTE
821985 
NaN
 
11946
None
 
698

Length

Max length11
Median length11
Mean length10.962815
Min length3

Characters and Unicode

Total characters29615551
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCON PATENTE
2nd rowCON PATENTE
3rd rowCON PATENTE
4th rowCON PATENTE
5th rowCON PATENTE

Common Values

ValueCountFrequency (%)
SIN PATENTE1866826
69.1%
CON PATENTE821985
30.4%
NaN11946
 
0.4%
None698
 
< 0.1%

Length

2025-10-17T23:25:22.651323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T23:25:22.689356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
patente2688811
49.9%
sin1866826
34.6%
con821985
 
15.2%
nan11946
 
0.2%
none698
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N5402212
18.2%
T5377622
18.2%
E5377622
18.2%
A2688811
9.1%
P2688811
9.1%
2688811
9.1%
S1866826
 
6.3%
I1866826
 
6.3%
C821985
 
2.8%
O821985
 
2.8%
Other values (4)14040
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)29615551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N5402212
18.2%
T5377622
18.2%
E5377622
18.2%
A2688811
9.1%
P2688811
9.1%
2688811
9.1%
S1866826
 
6.3%
I1866826
 
6.3%
C821985
 
2.8%
O821985
 
2.8%
Other values (4)14040
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)29615551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N5402212
18.2%
T5377622
18.2%
E5377622
18.2%
A2688811
9.1%
P2688811
9.1%
2688811
9.1%
S1866826
 
6.3%
I1866826
 
6.3%
C821985
 
2.8%
O821985
 
2.8%
Other values (4)14040
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)29615551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N5402212
18.2%
T5377622
18.2%
E5377622
18.2%
A2688811
9.1%
P2688811
9.1%
2688811
9.1%
S1866826
 
6.3%
I1866826
 
6.3%
C821985
 
2.8%
O821985
 
2.8%
Other values (4)14040
 
< 0.1%

indice_gse
Real number (ℝ)

Missing 

Distinct1360
Distinct (%)0.1%
Missing147275
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean0.10769463
Minimum0
Maximum0.61673068
Zeros32
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:22.739522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.045710314
Q10.070892012
median0.089680775
Q30.12638388
95-th percentile0.20828511
Maximum0.61673068
Range0.61673068
Interquartile range (IQR)0.055491867

Descriptive statistics

Standard deviation0.065459531
Coefficient of variation (CV)0.60782542
Kurtosis11.16769
Mean0.10769463
Median Absolute Deviation (MAD)0.024641158
Skewness2.7831225
Sum275071.46
Variance0.0042849502
MonotonicityNot monotonic
2025-10-17T23:25:22.793656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.199085232313475
 
0.5%
0.157787416112620
 
0.5%
0.0778520648210930
 
0.4%
0.084159010489575
 
0.4%
0.087306383379469
 
0.4%
0.17371725179420
 
0.3%
0.079092752669301
 
0.3%
0.095413439679002
 
0.3%
0.084279910968753
 
0.3%
0.067934205728153
 
0.3%
Other values (1350)2453482
90.8%
(Missing)147275
 
5.5%
ValueCountFrequency (%)
032
 
< 0.1%
0.0115555555626
 
< 0.1%
0.016333799861001
< 0.1%
0.01742628475917
< 0.1%
0.01762380952244
 
< 0.1%
0.01765070729887
< 0.1%
0.018420438961064
< 0.1%
0.022406655841489
0.1%
0.02309187279736
< 0.1%
0.02337123746265
 
< 0.1%
ValueCountFrequency (%)
0.61673067861057
< 0.1%
0.5621148523147
 
< 0.1%
0.54046443091609
0.1%
0.53396639471479
0.1%
0.5241336952700
 
< 0.1%
0.5131611595456
 
< 0.1%
0.50816055051024
< 0.1%
0.49198684722511
0.1%
0.47266911732333
0.1%
0.4457646212450
 
< 0.1%

n_habitantes
Real number (ℝ)

Missing 

Distinct1314
Distinct (%)0.1%
Missing147275
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean18812.004
Minimum0
Maximum99870
Zeros32
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:22.852573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2027
Q17760
median14995
Q324532
95-th percentile49429
Maximum99870
Range99870
Interquartile range (IQR)16772

Descriptive statistics

Standard deviation15798.619
Coefficient of variation (CV)0.83981588
Kurtosis5.4569102
Mean18812.004
Median Absolute Deviation (MAD)7763
Skewness1.9554495
Sum4.8049243 × 1010
Variance2.4959637 × 108
MonotonicityNot monotonic
2025-10-17T23:25:22.910762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2095413475
 
0.5%
7912412620
 
0.5%
5503110930
 
0.4%
998709575
 
0.4%
580809469
 
0.4%
98619420
 
0.3%
402629301
 
0.3%
970449002
 
0.3%
128808827
 
0.3%
154398753
 
0.3%
Other values (1304)2452808
90.8%
(Missing)147275
 
5.5%
ValueCountFrequency (%)
032
 
< 0.1%
7363
< 0.1%
1117
 
< 0.1%
12143
 
< 0.1%
1444
 
< 0.1%
23456
< 0.1%
31229
 
< 0.1%
32501
< 0.1%
3467
 
< 0.1%
46623
< 0.1%
ValueCountFrequency (%)
998709575
0.4%
970449002
0.3%
797555574
0.2%
7912412620
0.5%
739587444
0.3%
728885841
0.2%
616546531
0.2%
603322333
 
0.1%
602602743
 
0.1%
580809469
0.4%

n_ptos_interes
Real number (ℝ)

Missing 

Distinct172
Distinct (%)< 0.1%
Missing48031
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean55.938215
Minimum1
Maximum596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:22.966406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median29
Q366
95-th percentile201
Maximum596
Range595
Interquartile range (IQR)53

Descriptive statistics

Standard deviation78.189936
Coefficient of variation (CV)1.3977911
Kurtosis17.298578
Mean55.938215
Median Absolute Deviation (MAD)20
Skewness3.6123063
Sum1.484278 × 108
Variance6113.666
MonotonicityNot monotonic
2025-10-17T23:25:23.021747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
972539
 
2.7%
1665651
 
2.4%
1262056
 
2.3%
1357298
 
2.1%
656686
 
2.1%
553775
 
2.0%
252189
 
1.9%
850874
 
1.9%
450752
 
1.9%
1850693
 
1.9%
Other values (162)2080911
77.0%
ValueCountFrequency (%)
149191
1.8%
252189
1.9%
347823
1.8%
450752
1.9%
553775
2.0%
656686
2.1%
749525
1.8%
850874
1.9%
972539
2.7%
1032802
1.2%
ValueCountFrequency (%)
59613475
0.5%
5394074
 
0.2%
5012333
 
0.1%
4616399
0.2%
4464645
 
0.2%
3654652
 
0.2%
3614231
 
0.2%
3595628
0.2%
3433612
 
0.1%
2903470
 
0.1%

superficie_km2
Real number (ℝ)

High correlation  Missing 

Distinct1832
Distinct (%)0.1%
Missing48031
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean85.450577
Minimum0.10992095
Maximum17132.919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:23.079956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.10992095
5-th percentile0.75184132
Q11.664497
median8.4773824
Q339.873099
95-th percentile260.90565
Maximum17132.919
Range17132.809
Interquartile range (IQR)38.208602

Descriptive statistics

Standard deviation379.49883
Coefficient of variation (CV)4.44115
Kurtosis225.88235
Mean85.450577
Median Absolute Deviation (MAD)7.5171572
Skewness11.732091
Sum2.2673661 × 108
Variance144019.36
MonotonicityNot monotonic
2025-10-17T23:25:23.136691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.21264882513475
 
0.5%
767.998586812620
 
0.5%
64.8628070710930
 
0.4%
22.851097559575
 
0.4%
24.245936129469
 
0.4%
1.6537356019420
 
0.3%
3.8507410169301
 
0.3%
22.981896229002
 
0.3%
943.74875048753
 
0.3%
62.912803148153
 
0.3%
Other values (1822)2552726
94.5%
(Missing)48031
 
1.8%
ValueCountFrequency (%)
0.10992095331292
< 0.1%
0.150668958189
 
< 0.1%
0.2286851919327
 
< 0.1%
0.2662815307706
 
< 0.1%
0.2857682808388
 
< 0.1%
0.3062273498792
 
< 0.1%
0.31849379312481
0.1%
0.3213928872640
 
< 0.1%
0.3249334435279
 
< 0.1%
0.32528755312364
0.1%
ValueCountFrequency (%)
17132.9189332
 
< 0.1%
10403.7113524
 
< 0.1%
9104.65068912
 
< 0.1%
8827.694491143
 
< 0.1%
6473.267074133
 
< 0.1%
5013.03319358
 
< 0.1%
4804.26701420
 
< 0.1%
4753.380743103
 
< 0.1%
4475.3286441355
0.1%
4371.358449788
< 0.1%

densidad_hab
Real number (ℝ)

High correlation  Missing 

Distinct1360
Distinct (%)0.1%
Missing147275
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean5129.1223
Minimum0
Maximum45213.161
Zeros32
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:23.196036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.806969
Q1396.91462
median2849.0011
Q38257.1378
95-th percentile15951.572
Maximum45213.161
Range45213.161
Interquartile range (IQR)7860.2232

Descriptive statistics

Standard deviation6202.7494
Coefficient of variation (CV)1.2093198
Kurtosis9.126839
Mean5129.1223
Median Absolute Deviation (MAD)2724.9734
Skewness2.2966026
Sum1.3100702 × 1010
Variance38474100
MonotonicityNot monotonic
2025-10-17T23:25:23.250112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17279.5285613475
 
0.5%
103.026231212620
 
0.5%
848.42149910930
 
0.4%
4370.4684119575
 
0.4%
2395.4529839469
 
0.4%
5962.8637119420
 
0.3%
10455.649929301
 
0.3%
4222.6280669002
 
0.3%
16.359226968753
 
0.3%
833.07367328153
 
0.3%
Other values (1350)2453482
90.8%
(Missing)147275
 
5.5%
ValueCountFrequency (%)
032
 
< 0.1%
0.01553075957456
< 0.1%
0.05665253926143
 
< 0.1%
0.08198804269363
 
< 0.1%
0.184079341944
 
< 0.1%
0.1891772095114
 
< 0.1%
0.3240110898255
 
< 0.1%
0.398341675928
< 0.1%
0.398617634317
 
< 0.1%
0.5429295912569
< 0.1%
ValueCountFrequency (%)
45213.160775536
0.2%
43586.474535360
0.2%
42636.912654703
0.2%
37937.90242964
0.1%
29464.328134028
0.1%
25148.026931334
 
< 0.1%
22474.679341852
 
0.1%
22011.046491793
 
0.1%
21551.837951696
 
0.1%
21421.878342102
 
0.1%

uf
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37332.756
Minimum35287.5
Maximum39383.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:23.299270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35287.5
5-th percentile35509.68
Q136130.31
median37261.98
Q338384.41
95-th percentile39267.07
Maximum39383.07
Range4095.57
Interquartile range (IQR)2254.1

Descriptive statistics

Standard deviation1237.5139
Coefficient of variation (CV)0.033148206
Kurtosis-1.23108
Mean37332.756
Median Absolute Deviation (MAD)1131.67
Skewness0.11699254
Sum1.0085276 × 1011
Variance1531440.6
MonotonicityNot monotonic
2025-10-17T23:25:23.349740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
35575.4888568
 
3.3%
35287.587589
 
3.2%
35838.5586882
 
3.2%
36789.3686329
 
3.2%
35509.6886291
 
3.2%
36032.8986184
 
3.2%
38416.6985704
 
3.2%
37093.5285662
 
3.2%
36388.0785406
 
3.2%
36563.8785351
 
3.2%
Other values (22)1837489
68.0%
ValueCountFrequency (%)
35287.587589
3.2%
35509.6886291
3.2%
35575.4888568
3.3%
35838.5586882
3.2%
36032.8986184
3.2%
36049.0584431
3.1%
36089.4884554
3.1%
36130.3185270
3.2%
36197.5384846
3.1%
36388.0785406
3.2%
ValueCountFrequency (%)
39383.0782125
3.0%
39267.0780950
3.0%
39189.4582108
3.0%
39179.0181864
3.0%
39075.4182402
3.1%
38894.1184329
3.1%
38647.9482334
3.0%
38416.6985704
3.2%
38384.4184312
3.1%
38247.9284756
3.1%

dolar
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean909.78732
Minimum789.32
Maximum992.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:23.393643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum789.32
5-th percentile801.61
Q1854.22
median932.66
Q3951.21
95-th percentile988.1
Maximum992.12
Range202.8
Interquartile range (IQR)96.99

Descriptive statistics

Standard deviation63.182716
Coefficient of variation (CV)0.069447787
Kurtosis-0.98187847
Mean909.78732
Median Absolute Deviation (MAD)44.66
Skewness-0.59901171
Sum2.4577495 × 109
Variance3992.0556
MonotonicityNot monotonic
2025-10-17T23:25:23.448847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
789.3288568
 
3.3%
810.3787589
 
3.2%
801.6186882
 
3.2%
884.5986329
 
3.2%
831.2486291
 
3.2%
803.9486184
 
3.2%
992.1285704
 
3.2%
982.3885662
 
3.2%
910.2885406
 
3.2%
867.8685351
 
3.2%
Other values (22)1837489
68.0%
ValueCountFrequency (%)
789.3288568
3.3%
801.6186882
3.2%
802.6884554
3.1%
803.9486184
3.2%
810.3787589
3.2%
827.8484431
3.1%
831.2486291
3.2%
854.2285270
3.2%
867.8685351
3.2%
884.5986329
3.2%
ValueCountFrequency (%)
992.1285704
3.2%
988.184312
3.1%
982.3885662
3.2%
980.1984102
3.1%
978.0781864
3.0%
977.3284756
3.1%
967.4882125
3.0%
956.5883448
3.1%
951.2182334
3.0%
951.0281188
3.0%

ipc
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35194512
Minimum-0.5
Maximum1.1
Zeros82125
Zeros (%)3.0%
Negative505016
Negative (%)18.7%
Memory size20.6 MiB
2025-10-17T23:25:23.492990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile-0.4
Q10.1
median0.4
Q30.7
95-th percentile1.1
Maximum1.1
Range1.6
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.40921552
Coefficient of variation (CV)1.1627254
Kurtosis-0.59910165
Mean0.35194512
Median Absolute Deviation (MAD)0.3
Skewness-0.041077975
Sum950763.9
Variance0.16745735
MonotonicityNot monotonic
2025-10-17T23:25:23.535811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.7338982
12.5%
0.4337833
12.5%
0.1254939
9.4%
0.3253279
9.4%
0.2249266
9.2%
1.1172880
 
6.4%
-0.2170258
 
6.3%
0.5169203
 
6.3%
-0.1167479
 
6.2%
0.887589
 
3.2%
Other values (6)499747
18.5%
ValueCountFrequency (%)
-0.586329
 
3.2%
-0.480950
 
3.0%
-0.2170258
6.3%
-0.1167479
6.2%
082125
 
3.0%
0.1254939
9.4%
0.2249266
9.2%
0.3253279
9.4%
0.4337833
12.5%
0.5169203
6.3%
ValueCountFrequency (%)
1.1172880
6.4%
184377
 
3.1%
0.981864
 
3.0%
0.887589
 
3.2%
0.7338982
12.5%
0.684102
 
3.1%
0.5169203
6.3%
0.4337833
12.5%
0.3253279
9.4%
0.2249266
9.2%

imacec
Real number (ℝ)

High correlation  Zeros 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3097131
Minimum-2.1
Maximum6.6
Zeros168331
Zeros (%)6.2%
Negative686412
Negative (%)25.4%
Memory size20.6 MiB
2025-10-17T23:25:23.577428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.1
5-th percentile-2
Q1-0.1
median1.1
Q32.5
95-th percentile4.5
Maximum6.6
Range8.7
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation2.0132531
Coefficient of variation (CV)1.537171
Kurtosis-0.21270946
Mean1.3097131
Median Absolute Deviation (MAD)1.4
Skewness0.40586515
Sum3538131.1
Variance4.0531881
MonotonicityNot monotonic
2025-10-17T23:25:23.619415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2.5252051
 
9.3%
-1170883
 
6.3%
0168331
 
6.2%
2.3167661
 
6.2%
1.8166295
 
6.2%
-2.188568
 
3.3%
0.487589
 
3.2%
-1.186882
 
3.2%
-0.586291
 
3.2%
-286184
 
3.2%
Other values (16)1340720
49.6%
ValueCountFrequency (%)
-2.188568
3.3%
-286184
3.2%
-1.186882
3.2%
-1170883
6.3%
-0.985270
3.2%
-0.586291
3.2%
-0.182334
3.0%
0168331
6.2%
0.181188
3.0%
0.385406
3.2%
ValueCountFrequency (%)
6.685704
 
3.2%
4.584102
 
3.1%
4.283448
 
3.1%
3.884329
 
3.1%
3.584874
 
3.1%
3.282108
 
3.0%
3.180950
 
3.0%
2.5252051
9.3%
2.3167661
6.2%
2.184756
 
3.1%

tpm
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4377479
Minimum4.75
Maximum11.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:23.654137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.75
5-th percentile4.75
Q15
median6.5
Q310.25
95-th percentile11.25
Maximum11.25
Range6.5
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation2.4726703
Coefficient of variation (CV)0.33244879
Kurtosis-1.4187183
Mean7.4377479
Median Absolute Deviation (MAD)1.5
Skewness0.45469023
Sum20092741
Variance6.1140983
MonotonicityNot monotonic
2025-10-17T23:25:23.694160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
5582139
21.5%
11.25520068
19.3%
5.75247920
9.2%
8.25171666
 
6.4%
9170757
 
6.3%
7.25169764
 
6.3%
10.25169701
 
6.3%
5.25169133
 
6.3%
4.75163989
 
6.1%
6.584874
 
3.1%
Other values (3)251444
9.3%
ValueCountFrequency (%)
4.75163989
 
6.1%
5582139
21.5%
5.25169133
 
6.3%
5.583485
 
3.1%
5.75247920
9.2%
683113
 
3.1%
6.584874
 
3.1%
7.25169764
 
6.3%
8.25171666
 
6.4%
9170757
 
6.3%
ValueCountFrequency (%)
11.25520068
19.3%
10.25169701
 
6.3%
9.584846
 
3.1%
9170757
 
6.3%
8.25171666
 
6.4%
7.25169764
 
6.3%
6.584874
 
3.1%
683113
 
3.1%
5.75247920
9.2%
5.583485
 
3.1%

tasa_desempleo
Real number (ℝ)

Missing 

Distinct28
Distinct (%)< 0.1%
Missing82402
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean8.5768664
Minimum8.02
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2025-10-17T23:25:23.739769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8.02
5-th percentile8.04
Q18.38
median8.58
Q38.77
95-th percentile8.94
Maximum9
Range0.98
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation0.26348954
Coefficient of variation (CV)0.030720957
Kurtosis-0.52769328
Mean8.5768664
Median Absolute Deviation (MAD)0.19
Skewness-0.46629115
Sum22463268
Variance0.069426738
MonotonicityNot monotonic
2025-10-17T23:25:23.790704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
8.52171058
 
6.3%
8.68169110
 
6.3%
8.89166356
 
6.2%
8.8188568
 
3.3%
8.0487589
 
3.2%
8.6686882
 
3.2%
8.4886329
 
3.2%
8.3786291
 
3.2%
8.0885704
 
3.2%
8.7385351
 
3.2%
Other values (18)1505815
55.7%
ValueCountFrequency (%)
8.0284312
3.1%
8.0487589
3.2%
8.0885704
3.2%
8.2284756
3.1%
8.3183113
3.1%
8.3581188
3.0%
8.3786291
3.2%
8.3882334
3.0%
8.4485337
3.2%
8.4886329
3.2%
ValueCountFrequency (%)
985270
3.2%
8.9482108
3.0%
8.9184846
3.1%
8.983284
3.1%
8.89166356
6.2%
8.8188568
3.3%
8.7784431
3.1%
8.7483485
3.1%
8.7385351
3.2%
8.7184329
3.1%

Interactions

2025-10-17T23:25:10.775841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:23:50.276073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:23:55.657140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:01.039187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:06.287902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:11.410212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:16.502108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:21.497325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:26.919063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:32.359621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:37.904310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:43.282750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-17T23:24:11.722976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-17T23:24:21.849967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-17T23:25:05.895732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:11.860957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-17T23:24:52.176159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-17T23:25:03.053203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:08.736929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:14.737669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:23:53.928076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:23:59.408873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:04.604360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:09.796546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:14.884206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:19.917352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:25.242916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:30.618734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:36.090797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:41.507801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:47.117662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:52.532846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:58.007362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:03.432420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:09.068890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:15.101012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:23:54.267015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:23:59.740649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:04.942851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:10.127477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:15.224643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:20.233206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:25.582860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:30.983177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:36.441264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:41.839840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:47.431555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:52.873134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:58.343149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:03.818133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:09.409011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:15.478567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:23:54.611443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:00.072059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:05.276542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:10.451082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:15.544501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:20.550753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:25.919184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:31.322462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:36.835104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:42.168692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:47.740113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:53.208964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:58.657265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:04.168543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:09.746523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:15.830304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:23:54.953715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:00.387243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:05.621717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:10.779091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:15.864748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:20.858541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:26.253818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:31.657094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:37.204038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:42.537092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:48.047685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:53.541500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:58.968888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:04.488089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:10.068388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:16.185039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:23:55.302259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:00.721254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:05.957159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:11.093146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:16.179975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:21.171820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:26.578231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:31.979389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:37.554847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:42.899257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:48.378242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:53.880450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:24:59.309083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:04.823004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T23:25:10.416417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-17T23:25:23.840624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
canaldensidad_habdescr_flag_patentedolarid_barrioid_clienteid_comunaid_periodoimacecindice_gseipcliq_umn_habitantesn_ptos_interessegmentosuperficie_km2tasa_desempleotpmuf
canal1.0000.0580.0770.0030.0860.0530.0840.0020.0020.1740.0030.0900.1280.1901.0000.0320.0030.0040.005
densidad_hab0.0581.0000.040-0.0010.259-0.0600.2600.002-0.0000.371-0.0020.0880.3280.1380.060-0.8900.005-0.0010.002
descr_flag_patente0.0770.0401.0000.0030.1030.0370.0890.0020.0020.0670.0010.0020.1000.0340.4660.0090.0030.0030.003
dolar0.003-0.0010.0031.0000.0020.0250.0020.7660.6840.0040.1160.0140.0000.0040.0070.002-0.299-0.8060.756
id_barrio0.0860.2590.1030.0021.000-0.0790.9970.0040.0010.041-0.0000.0720.0890.1120.083-0.1890.001-0.0030.004
id_cliente0.053-0.0600.0370.025-0.0791.000-0.0780.0280.021-0.041-0.001-0.023-0.017-0.0240.0400.048-0.005-0.0280.028
id_comuna0.0840.2600.0890.0020.997-0.0781.0000.0030.0010.042-0.0000.0720.0880.1130.083-0.1900.001-0.0030.003
id_periodo0.0020.0020.0020.7660.0040.0280.0031.0000.6040.006-0.076-0.0220.0030.0040.0100.000-0.004-0.9910.998
imacec0.002-0.0000.0020.6840.0010.0210.0010.6041.0000.0040.2180.0040.0000.0030.0050.001-0.080-0.6180.600
indice_gse0.1740.3710.0670.0040.041-0.0410.0420.0060.0041.000-0.0010.0410.2120.4430.108-0.3540.002-0.0060.006
ipc0.003-0.0020.0010.116-0.000-0.001-0.000-0.0760.218-0.0011.0000.030-0.0020.0010.0040.0010.0530.034-0.116
liq_um0.0900.0880.0020.0140.072-0.0230.072-0.0220.0040.0410.0301.0000.099-0.0380.043-0.040-0.0460.013-0.022
n_habitantes0.1280.3280.1000.0000.089-0.0170.0880.0030.0000.212-0.0020.0991.0000.2220.0790.0560.004-0.0020.003
n_ptos_interes0.1900.1380.0340.0040.112-0.0240.1130.0040.0030.4430.001-0.0380.2221.0000.116-0.146-0.002-0.0040.004
segmento1.0000.0600.4660.0070.0830.0400.0830.0100.0050.1080.0040.0430.0790.1161.0000.0280.0070.0070.008
superficie_km20.032-0.8900.0090.002-0.1890.048-0.1900.0000.001-0.3540.001-0.0400.056-0.1460.0281.000-0.005-0.001-0.000
tasa_desempleo0.0030.0050.003-0.2990.001-0.0050.001-0.004-0.0800.0020.053-0.0460.004-0.0020.007-0.0051.0000.069-0.001
tpm0.004-0.0010.003-0.806-0.003-0.028-0.003-0.991-0.618-0.0060.0340.013-0.002-0.0040.007-0.0010.0691.000-0.986
uf0.0050.0020.0030.7560.0040.0280.0030.9980.6000.006-0.116-0.0220.0030.0040.008-0.000-0.001-0.9861.000

Missing values

2025-10-17T23:25:16.485755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-17T23:25:18.033786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-17T23:25:20.750659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

id_clienteid_periodoliq_umid_barrioid_comunasegmentocanaldescr_flag_patenteindice_gsen_habitantesn_ptos_interessuperficie_km2densidad_habufdolaripcimacectpmtasa_desempleo
0332023018.91264710120.07101.0BACONSUMOCON PATENTE0.08169815800.07.02.4224586522.30059935287.50810.370.80.411.258.04
133202302-0.46656710120.07101.0BACONSUMOCON PATENTE0.08169815800.07.02.4224586522.30059935509.68831.24-0.1-0.511.258.37
2332023031.06944710120.07101.0BACONSUMOCON PATENTE0.08169815800.07.02.4224586522.30059935575.48789.321.1-2.111.258.81
3332023042.21184710120.07101.0BACONSUMOCON PATENTE0.08169815800.07.02.4224586522.30059935838.55801.610.3-1.111.258.66
4332023051.02144710120.07101.0BACONSUMOCON PATENTE0.08169815800.07.02.4224586522.30059936032.89803.940.1-2.011.258.52
5332023060.60480710120.07101.0BACONSUMOCON PATENTE0.08169815800.07.02.4224586522.30059936089.48802.68-0.2-1.011.258.53
6332023071.31904710120.07101.0BACONSUMOCON PATENTE0.08169815800.07.02.4224586522.30059936049.05827.840.41.810.258.77
7332023080.94464710120.07101.0BACONSUMOCON PATENTE0.08169815800.07.02.4224586522.30059936130.31854.220.1-0.910.259.00
8332023091.08672710120.07101.0BACONSUMOCON PATENTE0.08169815800.07.02.4224586522.30059936197.53906.840.70.09.508.91
9332023101.94112710120.07101.0BACONSUMOCON PATENTE0.08169815800.07.02.4224586522.30059936388.07910.280.40.39.008.89
id_clienteid_periodoliq_umid_barrioid_comunasegmentocanaldescr_flag_patenteindice_gsen_habitantesn_ptos_interessuperficie_km2densidad_habufdolaripcimacectpmtasa_desempleo
27014457275182024115.43361030119.010301.0ALCOMPRASIN PATENTE0.18190821659.041.044.326199488.62750738247.92977.320.22.15.258.22
27014467275182024127.95121030119.010301.0ALCOMPRASIN PATENTE0.18190821659.041.044.326199488.62750738416.69992.12-0.26.65.008.08
27014477275182025016.44641030119.010301.0ALCOMPRASIN PATENTE0.18190821659.041.044.326199488.62750738384.41988.101.12.55.008.02
27014487275182025025.82721030119.010301.0ALCOMPRASIN PATENTE0.18190821659.041.044.326199488.62750738647.94951.210.4-0.15.008.38
27014497275182025037.30801030119.010301.0ALCOMPRASIN PATENTE0.18190821659.041.044.326199488.62750738894.11946.100.53.85.008.71
27014507275182025044.32961030119.010301.0ALCOMPRASIN PATENTE0.18190821659.041.044.326199488.62750739075.41945.420.22.55.00NaN
27014517275182025054.94881030119.010301.0ALCOMPRASIN PATENTE0.18190821659.041.044.326199488.62750739189.45937.370.23.25.008.94
27014527275182025062.69761030119.010301.0ALCOMPRASIN PATENTE0.18190821659.041.044.326199488.62750739267.07935.74-0.43.15.008.89
27014537275182025071.49761030119.010301.0ALCOMPRASIN PATENTE0.18190821659.041.044.326199488.62750739179.01978.070.91.84.758.70
27014547275182025084.20961030119.010301.0ALCOMPRASIN PATENTE0.18190821659.041.044.326199488.62750739383.07967.480.00.54.758.56